• Corpus ID: 186206582

Hand Orientation Estimation in Probability Density Form

  title={Hand Orientation Estimation in Probability Density Form},
  author={Kazuaki Kondo and Daisuke Deguchi and Atsushi Shimada},
Hand orientation is an essential feature required to understand hand behaviors and subsequently support human activities. In this paper, we present a new method for estimating hand orientation in probability density form. It can solve the cyclicity problem in direct angular representation and enables the integration of multiple predictions based on different features. We validated the performance of the proposed method and an integration example using our dataset, which captured cooperative… 

Figures and Tables from this paper

Hand disambiguation using attention neural networks in the egocentric perspective

An Attention Network with various egocentric hand properties is used to address the computer vision task of detecting the hands and disambiguating them left from right, using the YOLO object detector and its Tiny version.

Tenodesis Grasp Detection in Egocentric Video

This paradigm provides the first method that can enable clinicians and researchers to monitor the use of the tenodesis grasp by individuals with cSCI at home, with implications for remote therapeutic guidance.



A Light CNN based Method for Hand Detection and Orientation Estimation

A light CNN network, which uses a modified MobileNet as the feature extractor in company with the SSD framework to achieve a robust and fast detection of hand location and orientation, and employs a top-down feature fusion architecture that integrates context information across levels of features.

Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions

This work develops methods to locate and distinguish between hands in egocentric video using strong appearance models with Convolutional Neural Networks, and introduces a simple candidate region generation approach that outperforms existing techniques at a fraction of the computational cost.

Joint Hand Detection and Rotation Estimation Using CNN

A convolutional neural network (CNN), which formulates in-plane rotation explicitly to solve hand detection and rotation estimation jointly, and which outperforms the state-of-the-art detection models on widely-used benchmarks.

This Hand Is My Hand: A Probabilistic Approach to Hand Disambiguation in Egocentric Video

A probabilistic framework for modeling paired interactions that incorporates the spatial, temporal, and appearance constraints inherent in egocentric video is presented.

Hand detection using multiple proposals

The hand detector exceeds the state of the art on two public datasets, including the PASCAL VOC 2010 human layout challenge and is introduced with a fully annotated hand dataset for training and testing.

Robust Hand Detection and Classification in Vehicles and in the Wild

This work presents a novel approach named Multiple Scale Region-based Fully Convolutional Networks (MSRFCN) to robustly detect and classify human hand regions under various challenging conditions, e.g. occlusions, illumination, low-resolutions.

ConvNet Regression for Fingerprint Orientations

This work proposes to use Convolutional Neural Networks trained in a regression to estimate the orientation field (ConvNetOF) and achieves an RMSE of 8.53 on the Bad Quality Dataset of the FVC-ongoing benchmark, which is the best result reported so far.

OrieNet: A Regression System for Latent Fingerprint Orientation Field Extraction

A new algorithm system specific for fingerprint orientation estimation is proposed, combining domain knowledge of handcraft methods and the generalization ability of DNN.

Very Deep Convolutional Networks for Large-Scale Image Recognition

This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

Pyramid Scene Parsing Network

This paper exploits the capability of global context information by different-region-based context aggregation through the pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet) to produce good quality results on the scene parsing task.